Inferring chromosome segregation error stage and crossover in trisomic disorders with application to Down syndrome
Bibliographic record
Abstract
Errors in chromosome segregation during gametogenesis, such as nondisjunction (NDJ) errors, have severe consequences in human reproduction, and a better understanding of their etiology is of fundamental interest in genetics. Mapping NDJ errors to meiotic/mitotic stages typically requires proband-parent comparison, limiting its applicability. Herein, we develop Mis-segregation Error Identification through Hidden Markov Models (MeiHMM), a method for inferring NDJ error stage and crossover events based on only genomic data of trisomic probands. Guided by triallelic genotype/haplotype configurations, MeiHMM discerns the allelic origin at each locus, which informs NDJ error during gamete formation, without identifying the parental origin of the trisomy. In 152 Down syndrome (DS) cases, MeiHMM achieved an accuracy of 96.1% in classifying NDJ errors, with a sensitivity of 91.6% in crossover identification, compared to proband-parents trio analysis. 17% of Meiosis II errors were misclassified as Meiosis I, mainly due to small proximal crossover events. Applying MeiHMM to 509 children with DS-associated childhood leukemia, we demonstrate that NDJ error is associated with the age of disease onset, somatic genomic abnormalities, and prognosis. Thus, MeiHMM is an effective method for trisomic NDJ error classification and crossover identification that can be applied broadly to study the etiology of congenital aneuploidy conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".